System and method for magnetic resonance fingerprinting using a plurality of pulse sequence types

ABSTRACT

A method for performing magnetic resonance fingerprinting includes acquiring a plurality of MR image datasets using at least two pulse sequence types, the plurality of MR image datasets representing signal evolutions for image elements in a region of interest, comparing the plurality of MR image datasets to a dictionary of signal evolutions to identify at least one parameter of the MR image datasets and generating a report indicating the at least one parameter of the MR image datasets.

Method for generating a magnetic resonance dataset, computer program product, data storage medium and magnetic resonance system

The invention relates to a method for generating a magnetic resonance dataset comprising a plurality of image datasets, wherein the image datasets are acquired using a plurality of measurement sequences and represent a signal evolution over time for each image element.

The image contrast in magnetic resonance images depends on a plurality of parameters. These include tissue parameters, which depend on the patient or region under examination, e.g. the relaxation times T₁, T₂, the spin density, and diffusion-dependent parameters such as the ADC or flow rates. Device-dependent parameters may also be involved, however, for instance via the homogeneity or intensity of the main magnetic field B₀ or the intensity of the transmit field B₁.

These device-dependent parameters are meant to be eliminated by either the measurement setup or at least subsequent calculations because they lead to incorrect values for the patient-dependent parameters.

Average values of the parameters are associated with individual tissues. White brain matter has a specific T₁ value and T₂ value. This association can be made using always the same measurement sequence at least on one device.

Medical conditions can cause the patient-dependent parameters to change. Therefore there are a large number of studies that associate a medical condition with changes in a specific parameter: vascular constrictions manifest themselves as an increase in the flow rate; carcinomas alter the relaxation times, etc.

It is therefore useful for diagnosis and also treatment monitoring to quantify the patient-dependent parameters. Hence there are a large number of methods for quantifying each individual parameter. At least a dozen methods can be used to measure the T₁ relaxation time alone. For each method, the resultant T₁ time varies to a certain extent.

This, combined with the device-dependency of the tissue parameters, makes the informative value of the parameter maps, in relation to the time that must be spent on the measurement, too low for comprehensive usage. Instead it is common practice to acquire weighted images. This means that a spin echo having a short repetition time T_(R) is used to obtain a T₁-weighting. To obtain a T₂-weighting, on the other hand, a long echo time T_(E) is used. It is thereby possible to perform weightings for numerous tissue parameters in order to obtain the necessary image data in an acceptable time.

A more recent approach to quantifying parameters is what is called magnetic resonance fingerprinting, or MR fingerprinting or MRF for short. Ma D. et al., Magnetic resonance fingerprinting, Nature 495, p. 187-193 (2013), propose using pseudorandom measurement parameters. In this approach, measurement parameters are changed after an image is acquired in a bSSFP sequence. The specific parameters that are varied are the repetition time and the flip angle of the excitation pulse. The measurement is performed in one go, however, and therefore the final magnetization after acquisition of the first image constitutes the initial magnetization of the second image.

The measurement signals obtained in this manner produce a signal evolution for each image element of the acquired images. This signal evolution depends on T₁, T₂ and B₀.

The procedure for obtaining the parameters—two tissue parameters and a device parameter—from the signal evolutions is as follows:

For each parameter, the signal evolution is simulated for a set of defined values. Thus simulated signal evolutions are created for T₁ in increments of 100 ms, for instance, for T₂ in increments of 10 ms and for B₀ in increments of 0.1 Hz. If 50 values are used for T₁, 50 for T₂ and 100 for B₀, then the dictionary contains 250,000 entries. After the measurement, for each image element a search is made in the dictionary to find which of the simulated signal evolutions has the best fit to the measured signal evolution. This process is called matching. The values of T₁, T₂ and B₀ stored for the signal evolution that is the best fit are then those that are determined for the image element.

This procedure has several advantages. First, a plurality of of parameter maps are obtained with one measurement. Secondly, the device parameter B₀ can also be measured in addition to the tissue parameters T₁ and T₂. This eliminates the problem of the dependency of the tissue parameters on this value.

One disadvantage is that for the sequence used by Ma et al., the long spiral readout means that there is a lower limit to the repetition time T_(R), resulting in artifacts.

It is hence the object of the present invention to define a method for generating a magnetic resonance dataset, which method can be used to quantify a plurality of parameters simultaneously, produces image data containing as few artifacts as possible and also has a high SNR efficiency.

This object is achieved by a method for generating a magnetic resonance dataset comprising a plurality of image datasets, wherein the image datasets are acquired using a plurality of measurement sequences and represent a signal evolution over time, wherein the measurement comprises a plurality of stages, and in at least one stage a TrueFISP sequence is used to acquire image datasets, and in at least one stage a FLASH sequence is used to acquire image datasets.

The heart of the invention is considered to be that, instead of randomizing the measurement parameters of just a single sequence, and thereby obtaining a parameter-dependent signal evolution over a plurality of image datasets, a second sequence is used.

A sequence or measurement sequence denotes, as is customary, a series of radio-frequency pulses, gradient fields, delays and acquisition windows, which precisely define and characterize the execution of the measurement sequence. Examples of measurement sequences are the FLASH and TrueFisp sequences already mentioned. Other examples of measurement sequences are gradient echo, EPI, spin echo, TSE (Turbo Spin Echo), etc.

The bSSFP mentioned in the introduction is the acronym for balanced steady state free-precession, and is also known as TrueFISP. Like FISP as well, it is a sequence that uses longitudinal and transverse magnetization in the steady state. In contrast, only longitudinal magnetization is used in FLASH or SPGR sequences.

TrueFISP is understood to mean here a sequence design in which all the moments sum to 0 after a repetition time T_(R). With FISP, at least one of the moments is not balanced out.

This combination makes it possible to obtain the desired parameters simultaneously and with fewer artifacts.

Cartesian sampling of k-space can be used with all the measurement sequences. Alternatively, radial sampling can be used.

Spiral sampling of k-space can advantageously be used for at least one of the measurement sequences used. Preferably, spiral k-space sampling can be used for all the measurement sequences and for all the image datasets.

With spiral sampling, a complete image dataset can be acquired after each radio-frequency pulse. The difference in the sequences then lies solely in the gradients applied in a repetition time T_(R).

In this embodiment, the definition of the repetition time T_(R) is the usual definition, namely the time between two corresponding successive points in a series of radio-frequency pulses and signals. In this embodiment, phase cycles then lie over a plurality of image datasets and not, as is the case for segmented k-space sampling, over a plurality of k-space rows.

Advantageously, the spiral trajectories of at least two successive image datasets can be rotated or turned or otherwise varied in the acquisition slice without altering the resolution of the k-space points and/or the number of k-space points. Possible subsampling artifacts then become incoherent and do not affect the matching.

The image datasets are acquired without steady state preferably for at least one measurement sequence. Particularly preferably, more than half of the image datasets are acquired without steady state. Moreover, more than 75% can be acquired without steady state. All the image datasets can preferably be acquired without steady state.

With segmented k-space sampling, at least the majority of FLASH and TrueFISP image datasets are acquired in the steady state. This does not provide any information, however, when comparing a plurality of measurements, because the signal evolution basically stagnates.

In practice, this can be implemented for instance such that an image dataset is acquired by a FLASH sequence using a flip angle of 4°, and the next image dataset, also acquired by a FLASH sequence, using a flip angle of 6°. Single-shot sampling is possible for the spiral sampling described later. Then a change in the flip angle for successive radio-frequency pulses, and therefore also in repetition times T_(R), is sufficient to avoid a steady state.

A receive coil array can preferably be used for acquiring the image datasets. In other words, the image datasets are acquired using parallel imaging. Even greater subsampling can then be used in the k-space acquisition.

Preferably at least some of the image datasets have identical acquisition parameters, i.e. the same FoV, same orientation, same number of k-space measurement points, etc. Although the resolution can also always be adapted mathematically, this always results in inaccuracies.

It has already been mentioned in the introduction that measurement parameters can be distributed in a pseudorandom manner. Unlike the known prior art, the repetition time T_(R) is preferably kept constant in the proposed method.

For Cartesian sampling, the following embodiments result: in a first embodiment, it is kept constant in an image dataset, but can change for repeat runs of the same measurement sequence and especially when the measurement sequence is changed. In another embodiment, the repetition time T_(R) is also constant for repeat runs of the same measurement sequence. Thus all the FLASH sequences in one stage are acquired with the same repetition time T_(R). The repetition time T_(R) can change, however, when there is a switch to the TrueFISP sequence or vice versa. It can also be different in a later stage when the FLASH sequence is used again. In a third embodiment, the repetition time T_(R) is constant during the entire measurement signal acquisition.

For spiral sampling, in which a complete image dataset is acquired after one radio-frequency pulse, the first embodiment does not apply. Then the repetition time T_(R) can preferably be kept constant either in each stage or over all the stages.

This is a change in the strategy in the sense that the signal evolution no longer gains a characteristic profile by constant changes to a plurality of measurement parameters but by a change in the measurement sequence.

Following this line, image datasets can advantageously be acquired in at least one stage using a FISP sequence. A FISP sequence affects the signal evolution again differently from FLASH or TrueFISP, and thus helps to make it possible to differentiate between more parameters.

Advantageously, at least one parameter of the subject under examination can be determined from at least one signal evolution. This makes it clear that the described method forms the basis for determining, i.e. quantifying, a tissue parameter, for instance T₁ or T₂.

It is hence implicitly suggested that the described method is based on the idea of MR fingerprinting. In conventional quantifying methods, a single measurement sequence is used and a single measurement parameter, for example T_(E), is varied in order to determine a tissue parameter such as T₂. It is not intended here to rule out using the proposed method to obtain tissue parameters also in a different way.

For encoding the parameters to be determined, preferably the flip angle is varied as the sole measurement parameter in at least one measurement sequence. As stated earlier, the parameters to be determined may be B₀, B₁, T₁, T₂, ADC, etc. Thus neither the repetition time T_(R) nor the echo time T_(E) are varied.

Alternatively or additionally, the flip angle and the echo time T_(E) are varied as the sole measurement parameters in at least one measurement sequence.

Preferably, the flip angle can be varied as the sole measurement parameter in all the measurement sequences except the FISP measurement sequence. Preferably, the flip angle and the echo time T_(E) can be varied as the sole measurement parameters for the FISP measurement sequence.

In this connection, the phase of the radio-frequency pulses needs to be discussed briefly. This is also a measurement parameter and varies when using a phase cycle, as explained later. It is, however, a variable that is changed in order to compensate for device shortcomings or that belongs intrinsically to a measurement sequence. Such measurement parameters are varied even when parameters are not quantified. By definition, a phase cycle is thus not one of the measurement parameters to be changed. Nevertheless, the described phase cycles are part of the invention, namely as a second value that can be varied in addition to the measurement parameters.

To be more precise, for encoding the parameters to be determined, the flip angle is therefore intended to be varied as the sole measurement parameter that affects the measurement signal as a function of at least one parameter. In other words, for encoding the parameters to be determined, the flip angle α shall be varied as the sole measurement parameter from the group repetition time T_(R), echo time T_(E) and flip angle α. This applies in particular to the FLASH and/or TrueFISP measurement sequence.

Alternatively, as described for the FISP measurement sequence, for encoding the parameters to be determined, the flip angle α and the echo time T_(E) shall be varied as the sole measurement parameters from the group repetition time T_(R), echo time T_(E) and flip angle α.

The flip angle can follow a predetermined distribution over a plurality of image datasets. It is thus varied no longer in a pseudorandom manner but using a strategy. In one embodiment, the flip angle can follow a normal distribution. It therefore starts with no flip angles. The flip angle increases up to a maximum value and then decreases again.

In another embodiment, the distribution is embodied as a half-sine curve, in particular the positive half. It rises more steeply than a normal distribution and has a wider plateau in the maximum region.

In another embodiment, the distribution is embodied as a half-sine² curve, in particular the positive half. This rises even more steeply than a sine curve.

Preferably there is at least one distribution in a stage. In particular there is precisely one distribution in at least one stage. In addition, there may be precisely two distributions in one stage.

A normal distribution can advantageously be followed in a stage in which image datasets are acquired using a FLASH measurement sequence.

Preferably the flip angles can follow a sine distribution in a stage in which image datasets are acquired using a FISP or TrueFISP measurement sequence.

It is preferred, however, that at least one signal evolution is compared with a plurality of simulated signal evolutions, where the simulation has been created by varying at least one tissue parameter and/or at least one device parameter. The simulated signal evolution that is the closest match to the measured signal evolution then determines the tissue parameter(s) and/or device parameter(s).

Advantageously, a B₀ value, a B₁ value, a T₁ value and a T₂ value can be determined from at least one signal evolution. Using the method described, it is therefore possible for the first time to determine the two device parameters B₀ and B₁ in one measurement. The tissue parameters T₁ and T₂ are obtained as well.

Preferably a plurality of image datasets as part of the magnetic resonance dataset are acquired in immediate succession using each sequence. Thus a plurality of TrueFISP image datasets can be acquired in immediate succession and afterwards a plurality of FLASH image datasets in immediate succession.

Some of the image datasets are preferably measured immediately one after the other. In other words, the final magnetization can be adopted by the sequence that follows. As already explained, the key point is to generate signal evolutions that exhibit variations depending on the measurement sequences and the parameters to be determined. These variations are smaller when there are measurement pauses, because the acquired measurement signal then depends more heavily on the initial magnetization M₀ than on the stages previously performed.

Advantageously, all the image datasets in a stage are measured immediately one after the other. In principle, any length of pause can exist between two stages.

Preferably, all the measurement sequences or image datasets, i.e. even when there is a change in measurement sequence or change in stage, are measured immediately one after the other.

For example, a FLASH having a large flip angle can be used to acquire an image dataset for a relatively short T_(R). Although this already provides information about T₁, this information is improved if the subsequent measurement, a FLASH with a smaller flip angle, starts with the magnetization at the end of the preceding FLASH compared with when the magnetization relaxes back to M₀ as a result of a pause.

In addition, a measurement containing pauses no longer constitutes a signal evolution but just individual measurement points.

Using a plurality of measurement sequences, however, still does not in itself result in the optimum procedure for managing with the fewest possible image datasets in the magnetic resonance dataset. A preferred embodiment has been found to be that of using first a FISP sequence, then a TrueFISP sequence and then a FLASH sequence in three successive stages. This means that first a plurality of FISP image datasets are acquired, where in principle any number of image datasets can be acquired in one stage provided it is done using the same measurement sequence. This does not mean, however, that all the measurement parameters of the sequence would have to stay the same in one stage. A variation, for instance to prevent a steady state, is allowed here. As already explained in detail, it is preferred to vary the flip angle.

Then follows a stage in which one or more image datasets are acquired using a TrueFISP sequence and then a stage using a FLASH sequence. In the last stage as well, a plurality of image datasets are acquired using the FLASH sequence.

This series comprising FISP sequence, TrueFISP sequence and FLASH sequence is referred to below as a block.

The described series, i.e. the block, can advantageously be repeated at least once. The block is thus performed at least twice. Preferably the series is performed precisely three times.

Advantageously at least 10 image datasets can be acquired using each sequence. Preferably at least 10 image datasets are acquired per stage. Then for three blocks at least 90 image datasets are obtained. This is a substantially larger number of sample points in the signal evolution than in conventional parameter maps, in which there are often only six to ten sample points for reasons of time.

Advantageously, in at least one stage, the TrueFISP sequence can comprise at least one phase cycle. A phase cycle is understood to mean the defined progression of the phase for specific radio-frequency pulses or all radio-frequency pulses. This is a variable that does not depend on the flip angle.

Preferably a 180° phase cycle is involved. Then the phases of the radio-frequency pulses in the TrueFISP sequence alternate from x to −x or from y to −y and back.

Alternatively, the phase cycle can be designed to be a 90° phase cycle. Then the phases can change, for example, from x to y to −x to −y and then from the beginning again.

As another alternative, the phase cycle can be designed to be a 270° phase cycle. In this case, one phase cycle is preferably used per distribution. Thus for three distributions of the flip angle, three different phase cycles can also be used. For more distributions, it is also possible to use more phase cycles.

Advantageously, two phase cycles can be used in one stage with the TrueFISP sequence. A 180° phase cycle can be used as one phase cycle and a 90° phase cycle as another phase cycle. Preferably, the 180° phase cycle can be used as the first phase cycle. It is possible to vary cycle-induced artifacts such as the position of the “dark band”, or avoid said artifacts, by changing the phase cycle.

Advantageously, a predetermined phase is used with the FISP sequence. The same phase is preferably used whenever a FISP sequence is started.

RF spoiling can be employed advantageously when using the FLASH sequence. RF spoiling means using a phase cycle that prevents potential T₂-weighting of the magnetization. The add-on phase can preferably equal 117° or a multiple thereof. In this case, the phase to be used is obtained from the previous phase by adding a multiple of 117°. The very first phase can be chosen to have any value and does not have to be a multiple of 117°. 117°, as a multiple of one, also counts as a multiple.

The object mentioned in the introduction is also achieved by a computer program product or a computer program which can be used to control a controller that controls an image generating unit of a magnetic resonance system, which image generating unit performs the aforementioned method according to the invention.

The invention also relates to a data storage medium for a controller for controlling a data generating unit of a magnetic resonance system using data for performing the described method. The data generating unit can advantageously be an image generating unit.

In addition, the invention relates to a magnetic resonance system comprising a controller. The magnetic resonance system is characterized in that the controller is designed to perform the method as described.

The aforementioned methods can be implemented in the controller as software or even as (hard-wired) hardware.

Further advantageous embodiments of the magnetic resonance system according to the invention correspond to the equivalent embodiments of the method according to the invention. To avoid unnecessary repetition, reference is therefore made to the corresponding method features and their advantages.

Further advantages, features and specific aspects of the present invention appear in the following description of exemplary embodiments of the invention, in which:

FIG. 1 shows a magnetic resonance system;

FIG. 2 shows a FLASH measurement sequence;

FIG. 3 shows a FISP measurement sequence;

FIG. 4 shows a TrueFISP measurement sequence;

FIG. 5 shows an acquisition method comprising a plurality of measurement sequences;

FIG. 6 shows a magnetic resonance dataset acquired using a FISP measurement sequence;

FIG. 7 shows a magnetic resonance dataset acquired using a TrueFISP measurement sequence;

FIG. 8 shows a magnetic resonance dataset acquired using a FLASH sequence.

FIG. 1 shows a magnetic resonance system 1 comprising a transmit coil arrangement 2. The transmit coil arrangement 2 can be in the form of a body coil. It may also be a transmit coil array, however. The transmit coil arrangement 2 is shown dashed.

The magnetic resonance system 1 has a receive coil arrangement 3 for the purpose of data acquisition. The receive coil arrangement 3 is preferably a coil array comprising coils 4, 5, 6 and 7. The coils 4, 5, 6 and 7 read out measurement signals simultaneously and thus in parallel.

The magnetic resonance system 1 comprises a controller 8 for controlling the trials.

The magnetic resonance system 1 also has a data storage medium 9 forming part of the controller 8 or separate therefrom, on which are stored computer programs 10 for performing magnetic resonance measurements.

Other parts of the magnetic resonance system 1 such as gradient coils or a patient couch, for instance, are not shown for reasons of clarity.

FIG. 2 shows a FLASH measurement sequence diagram 11. The gradient axes are labeled, in accordance with convention, with G_(R) for the readout direction, Gp for the phase encoding direction and G_(S) for the slice selection direction. ACQ denotes the axis for the radio-frequency pulses and the acquisition windows.

A FLASH is a sequence based on a gradient echo and containing an radio-frequency pulse 12 that has a flip angle of less than 90°. A T₂*-contrast can be adjusted by means of the echo time T_(E), and a T₁-contrast by means of the repetition time T_(R). The radio-frequency pulse typically has a flip angle between 4° and 30° for weighted measurements.

In order to excite just one slice using the radio-frequency pulse 12, a slice selection gradient 13 is applied in the slice selection direction G_(S) at the same time as the radio-frequency pulse 12. A slice rephasing gradient 14 follows immediately after the slice selection gradient 13 to correct the dephasing effect of the slice selection gradient on the magnetization in the transverse plane.

A phase encoding gradient 15 is used in the phase encoding direction G_(P). This gradient is applied in an oscillating manner, as is the readout gradient 16 in readout direction G_(R). This is done preferably to sample k-space in a spiral pattern.

Alternatively, as already described earlier, Cartesian or radial sampling can also be performed.

Measurement signals 17 can be acquired accordingly.

It is very important in this context that an entire image dataset is acquired in the repetition time T_(R). The FLASH measurement sequence 11 is thus a type of single-shot sequence, as is also the case for the other measurement sequences discussed, because a single radio-frequency pulse 12 is sufficient to obtain a complete image dataset.

The raw dataset acquired in this way can be converted into an image dataset by a non-uniform Fourier transform. This may be subject to artifacts, but this is adequate for matching.

The second radio-frequency pulse 12 on the right in the figure shows that the second image dataset is started after the acquisition of the first image dataset without a pause. The second radio-frequency pulse 12 can have a different flip angle from the first radio-frequency pulse 12, as explained in more detail later. Moreover, the phase can change in order to implement a phase cycle.

Parallel imaging can reduce SNR problems, because in this case less k-space data is acquired and this means that the repetition time T_(R) can be reduced.

FIG. 3 shows a FISP measurement sequence 18. In this sequence, again only one radio-frequency pulse 19 is used to acquire a complete image dataset.

A slice selection gradient 13, a slice rephasing gradient 14,phase encoding gradient 15 and a readout gradient 16 are applied, as described for the FLASH measurement sequence 11.

A phase rewind gradient 21 is additionally present. This ensures that the sum of the gradient moments in the phase direction equals zero over one repetition time T_(R).

In the slice direction G_(S), the sum of the gradient moments does not equal zero over one repetition time T_(R).

The gradients are balanced in the readout direction G_(R), but this is not mandatory. Thus the sum of the gradients in the readout direction G_(R) may also not equal 0 over one repetition time. Since spiral trajectories are acquired, the resultant sum moment is always the same because the individual gradient moments always have the same variation over one repetition time.

The second image dataset is started by the radio-frequency pulse 20. This preferably has the same phase as the previous radio-frequency pulse 19 but a different flip angle.

FIG. 4 shows a TrueFISP measurement sequence 22. Basically reference can be made here to the comments relating to the FLASH measurement sequence 11 and also the FISP measurement sequence 18.

In addition to the elements already mentioned, the TrueFISP measurement sequence 22 also contains a readout rewind gradient 23 and a slice dephasing gradient 24. The TrueFISP-measurement sequence 22 is thereby “fully balanced” over one repetition time T_(R), i.e. the sums of the gradient moments over one repetition time T_(R) are equal to zero in all directions.

The magnitudes of the flip angles of the radio-frequency pulses 19 and 20 again vary in the TrueFISP measurement sequence 22.

The TrueFISP measurement sequence 22 can comprise phase cycles, as already described. A 90° phase cycle can be used, as already described. In this case, the first radio-frequency pulse 19 has a phase φ, the second radio-frequency pulse 20 has a phase φ+180°, the third radio-frequency pulse 25 has a phase φ+90°, the fourth radio-frequency pulse has a phase φ+270°, the fifth radio-frequency pulse has a phase φ+180°, the sixth radio-frequency pulse has a phase φ+360°, etc.

A 180° phase cycle jumps in steps of 180° instead of in steps of 90°, and a 270° phase cycle jumps in steps of 270°.

The diagram for acquiring an image dataset, and also the radio-frequency pulse together with gradients in the slice selection direction G_(S) for the next image dataset, are shown for all the measurement sequences 11, 18 and 22 in order to illustrate the process.

FIG. 5 shows schematically an acquisition method for acquiring a magnetic resonance dataset. In this figure, the number of the acquired image dataset is plotted on the axis 26, and different variables are plotted on the axis 27. The flip angle in ° is plotted as the first variable from 0° at the origin to 90° at the axis point 28. The axis 26 extends from the image dataset 1 to the image dataset 3100.

The 3000 image datasets are distributed over twelve stages 29, 30, 31, 32, 33, 34, 35, 36, 37, 38 and 39.

In the first stage 29, the curve 40 plots for two hundred image datasets the flip angle used during the acquisition in the FISP measurement sequence 18. As was explained with regard to FIG. 3, a complete image dataset is acquired after an radio-frequency pulse having a specific flip angle is applied, and then the next radio-frequency pulse having the next flip angle is applied and another image dataset acquired. FIG. 5 accordingly shows in stage 29 a flip-angle distribution, which corresponds to a half-sine² curve. The maximum flip angle is 24° in magnitude and constant phases are used.

A line 41 is indicated for the hundredth image dataset purely by way of example. The corresponding flip angle is the maximum flip angle of the curve 40.

In the second stage 30, four hundred image datasets are acquired using the TrueFISP sequence 22 according to FIG. 4. Flip angles given by the curves 42 and 43 are used in this case. These flip angles reach 45° for the curve 42, and 72° for the curve 43.

Again for the stage 30, a line 44 is shown purely by way of example at the flip angle for the four-hundredth image dataset. The flip angle equals 1° here.

A particular feature in stage 30 is the use of two different phase cycles. A 00 phase cycle, i.e. no phase cycle, is used when running through the flip angles of the curve 42, and a 180° phase cycle is used when running through the curve 43. A 00 phase cycle denotes a fixed phase.

In the subsequent stage 31, the flip angles for acquiring four-hundred-and-fifty image datasets using a FLASH sequence 11 are shown in the curve 45. These are smaller than in the FISP or TrueFISP sequence and reach 6°. They too have a sine² distribution.

In addition to varying the flip angles, a phase cycle for achieving RF spoiling is applied during the repeated run-through of the FLASH sequence. In this case, the phase is increased by multiples of 117°, as already described.

The series of the measurement sequences 11, 18 and 22 together form a block 45. This is used a total of three times in FIG. 5. The block is based solely on the type of the measurement sequence but not on the number of image datasets or on the flip-angle curves.

In the stage 32, again 200 image datasets are acquired using a FISP sequence 18. As in stage 29, the phase is constant but the maximum flip angle equals 45°. These lie on the curve 46.

200 image datasets acquired using a TrueFISP sequence 22 follow in stage 33. In this case, a 90° phase cycle is used, and the maximum flip angle equals 50°. The flip angles are plotted on the curve 47.

The next approximately 450 image datasets in stage 34 are for acquisition using a FLASH sequence, as in stage 31. The curve 48 shows a sine² distribution with a maximum value of 14°.

Curve 49 in stage 35 reaches 72° and shows the flip angles of the radio-frequency pulse 19 for the third-time use of a FISP sequence 18. The phase is again constant in this run-through.

A 270° phase cycle is employed during acquisition of a further two hundred image datasets using a TrueFISP sequence 22 according to FIG. 4. The flip angles plotted in the curve 50 in stage 36 reach 65°.

The next approximately 450 image datasets in stage 37 are acquired using the FLASH sequence 11 according to FIG. 2. The curve 51 represents a flip-angle variation up to a maximum of 20°, again with a sine² distribution.

In the last stage stage 38 are two curves 52 and 53 for acquiring image datasets using a FISP sequence. These again represent flip-angle variations. A constant phase is used for the FISP measurement sequence 18, as was the case in the previous stages.

To summarize, it can be stated that irrespective of the specific number of images and the respective maximum flip angles, a flip-angle variation having a sine² distribution is preferably used in all the stages. As described earlier, far fewer image datasets can also be acquired in one stage, although preferably at least 10.

FIGS. 6 to 8 show a magnetic resonance dataset 54 comprising image datasets 55, 56 and 57. These are examples of the 3000 image datasets obtained in the procedure according to FIG. 5. The necessary postprocessing is sufficiently known.

The image datasets 55, 56 and 57 each depict a region under examination 58. The image dataset 55 was acquired using the FISP measurement sequence 18, image dataset 56 using the TrueFISP measurement sequence 22, and image dataset 57 using the FLASH sequence 11. In each case, the flip angle is one of the possible flip angles from the curves 40 to 53. The signal also depends on the history experienced in each case, however.

The analysis is performed for each image element. The image element 59 is indicated purely by way of example. In all the image datasets 55 to 57, the image element at the same position, namely the image element 59, is used to obtain a signal evolution. A signal evolution is determined and analyzed for each of the other image elements. The regions 60 in which only noise signal is present can be identified using a threshold value, for instance, and omitted in order to minimize the analysis time. 

1. A method for performing magnetic resonance fingerprinting comprising: acquiring a plurality of MR image datasets using at least two pulse sequence types, the plurality of MR image datasets representing signal evolutions for image elements in a region of interest; comparing the plurality of MR image datasets to a dictionary of signal evolutions to identify at least one parameter of the MR image datasets; and generating a report indicating the at least one parameter of the MR image datasets.
 2. The method according to claim 1, wherein at least one of the at least two pulse sequence types is a FISP pulse sequence.
 3. The method according to claim 1, wherein at least one of the at least two pulse sequence types is a TrueFISP pulse sequence.
 4. The method according to claim 1, wherein at least one of the at least two pulse sequence types is a FLASH pulse sequence.
 5. The method according to claim 1, wherein acquiring a plurality of MR image datasets using at least two pulse sequence types comprises: acquiring a first plurality of MR image datasets using a first pulse sequence type during a first stage; and acquiring a second plurality of MR image datasets using a second pulse sequence type during a second stage.
 6. The method according to claim 5, wherein the first pulse sequence type is a TrueFISP pulse sequence and the second pulse sequence type is a FLASH pulse sequence.
 7. The method according to claim 5, wherein acquiring a plurality of MR image datasets using at least two pulse sequence types further comprises acquiring a third plurality of MR image datasets using a third pulse sequence type during a third stage.
 8. The method according to claim 7, wherein the first pulse sequence type is a FISP pulse sequence, the second pulse sequence type is a TrueFISP pulse sequence and the third pulse sequence type is a FLASH pulse sequence.
 9. The method according to claim 8, wherein the first stage, second stage and third stage are successive stages.
 10. The method according to claim 1, wherein in at least one of the at least two pulse sequence types a flip angle is varied during acquisition of the plurality of MR image datasets.
 11. The method according to claim 1, wherein in at least one of the at least two pulse sequence types a phase cycle is varied during acquisition of the plurality of MR image datasets.
 12. The method according to claim 8, wherein a series of the FISP pulse sequence, the TrueFISP pulse sequence and the FLASH pulse sequence is repeated at least once.
 13. A magnetic resonance imaging (MRI) system comprising: a magnet system configured to generate a polarizing magnetic field about at least a portion of a subject; a magnetic gradient system including a plurality of magnetic gradient coils configured to apply at least one magnetic gradient field to the polarizing magnetic field; a radio frequency (RF) system configured to apply an RF field to the subject and to receive magnetic resonance signals from the subject using a coil array; and a computer system programmed to: acquire a plurality of MR image datasets using at least two pulse sequence types, the plurality of MR image datasets representing signal evolutions for image elements in a region of interest; compare the plurality of MR image datasets to a dictionary of signal evolutions to identify at least one parameter of the MR image datasets; and generate a report indicating the at least one parameter of the MR image datasets.
 14. The system according to claim 13, wherein at least one of the at least two pulse sequence types is a FISP pulse sequence.
 15. The system according to claim 13, wherein at least one of the at least two pulse sequence types is a TrueFISP pulse sequence.
 16. The system according to claim 13, wherein at least one of the at least two pulse sequence types is a FLASH pulse sequence.
 17. The system according to claim 13, wherein the computer system is further programmed to: acquire a first plurality of MR image datasets using a first pulse sequence type during a first stage; and acquire a second plurality of MR image datasets using a second pulse sequence type during a second stage.
 18. The system according to claim 17, wherein the computer system us further programmed to acquire a third plurality of MR image datasets using a third pulse sequence type during a third stage.
 19. The system according to claim 13, wherein in at least one of the at least two pulse sequence types a flip angle is varied during acquisition of the plurality of MR image datasets.
 20. The system according to claim 18, wherein the first pulse sequence type is a FISP pulse sequence, the second pulse sequence type is a TrueFISP pulse sequence and the third pulse sequence type is a FLASH pulse sequence. 